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Bioinspired double-layer thermogalvanic cells with engineered ionic gradients for high-efficiency waste heat recovery

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Nano Energy 2025年 142卷

作者： Chi, Cheng Zhang, Xingyu Shen, Chen Hu, Qi Liu, Ze Hu, Jiahao Li, Zhi Li, Yang Yu, Xiaoli Xiao, Hao Zhao, Zhaoquan Yao, Yuan Liang, Xing Wu, Hongwei Du, Xiaoze Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education School of Energy Power and Mechanical Engineering North China Electric Power University Beijing102206 China State Key Laboratory of Clean Energy Utilization Department of Energy Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China School of Computer Science and Mathematics Kingston University London KT1 2EE United Kingdom School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom

Thermogalvanic cells (TGCs) have emerged as a promising technology for harvesting low-grade thermal energy, but their widespread application has been hindered by limited conversion efficiencies. A critical factor in enhancing TGC performance lies in establishing substantial ion concentration gradients, which remains challenging due to the inherent tendency of ion pairing. Here, we present a double-layer thermogalvanic cell (DTGC) architecture that spatially segregates redox pairs into two distinct gel layers, enabling unprecedented control over ion concentration gradients. This innovative design yields a single p-type gelatin-K4[Fe(CN)6]/K3[Fe(CN)6] DTGC unit with remarkable performance metrics of an open-circuit voltage of 220 mV, a power density of 1.73 mW m−2 K−2, and a relative Carnot efficiency (ηr) of 1.34 % at ΔT = 10 K, representing a tenfold improvement over conventional TGCs. Scaling up this technology, we demonstrate a modular thermoelectric generator comprising a 4 × 12 array of alternating p-type and n-type DTGCs, capable of delivering an output voltage exceeding 11.3 V at ΔT = 20 K, sufficient to directly power commercial LED lights and electronic displays. This work establishes a new paradigm for efficient low-grade thermal energy conversion, offering a scalable and practical solution for waste heat recovery applications. © 2025 Elsevier Ltd

关键词： Waste heat utilization

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Co-Designed Communication and Computing for Data Reliability in Industrial Cyber-Physical systems with Cloud-Fog Automation

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IEEE Journal on Selected Areas in Communications 2025年

作者： Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao Zhu, Chenlu Zhou, Xinlei Yi, Lingzhi Wu, Libing Park, Jong Hyuk Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Wuhan Research Institute of Posts and Telecommunications China Zhongnan University of Economics and Law School of Information Engineering China Wuhan University School of Cyber Science and Engineering China Seoul National University of Science and Technology Department of Computer Science and Engineering Korea Republic of

The Cloud-Fog Automation is a newly proposed digital industrial automation architecture aimed at accelerating the integration and collaboration of communication, computing, and control towards next-generation cyber-physical systems (CPSs). Data reliability is one of the key considerations for achieving Cloud-Fog Automation. Sensor nodes serve as infrastructures for data collection within industrial CPSs and are essential for maintaining ultra-high data reliability. However, the underlying sensor nodes communicate frequently, are damage-prone and difficult to identify, which dramatically shortens the network lifetime and poses great challenges to data reliability. Motivated by this fact, this paper co-designs communication architecture, algorithms, and computing models for next-generation industrial CPSs with Cloud-Fog Automation to ensure data reliability and functional security. First, a four-layer energy-efficient communication architecture is proposed and a cluster head computing algorithm based on double deep Q-learning (CH-DDQ) is designed inside the architecture. Besides, a 2-stage hyBrid fault detection scheme (2-Brain) is proposed for underlying sensor nodes. 2-Brain first incorporates the Obstacle Triple Jump Protocol (OTP) and OTP packets to improve hard fault detection performance. Then, an unsupervised sensor reading soft fault detection model (SR-SFD) based on contrastive learning, momentum, and tensor is adopted to learn discriminative representations of sensor readings and identify soft faults. Simulations and a case study in the nuclear power industry manifest CH-DDQ improves the network lifetime by 5.4%484.3% compared to three peer methods, and OTP performs better than baselines by 33.1% on average. Additionally, SR-SFD exhibits high efficiency in sensor soft fault detection and other application scenarios. © 1983-2012 IEEE.

关键词： Cluster computing

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Low-Complexity Chase Decoding of Elliptic Codes

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IEEE Transactions on Communications 2025年

作者： Wan, Yunqi Liang, Jiwei Chen, Li Zhang, Fangguo Sun Yat-sen University School of Electronics and Information Technology Guangzhou510006 China Sun Yat-sen University School of System Science and Engineering Guangzhou510006 China Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou510006 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China

This paper proposes two low-complexity Chase (LCC) decoding algorithms for elliptic codes, which are realized by Kötter’s interpolation and the basis reduction (BR) interpolation, respectively. They are both developed from the perspective of computing the Gröbner bases of the interpolation modules. By identifying η unreliable symbols, 2η decoding test-vectors are formulated and the corresponding interpolation modules can be defined. The re-encoding transform (ReT) is further introduced to facilitate the interpolation. The LCC-Kötter decoding performs interpolation for the common elements, producing an intermediate outcome shared by all test-vectors. The desired Gröbner basis w.r.t. each test-vector can be obtained in a binary tree growing fashion. The new interpolation process can start from intermediate nodes of the previously interpolated paths, resulting in a low complexity. But the decoding latency cannot be contained. In contrast, the LCC-BR decoding performs the common computation in basis construction, which partly substantiates the bases for all interpolation modules. The subsequent basis construction and reduction can be performed in parallel. Besides a low complexity, it offers a latency advantage over the LCC-Kötter decoding. The decoding complexity and latency are analyzed and verified numerically. The LCC decoding performance are also presented, demonstrating their advantage over both the Guruswami-Sudan decoding and the algebraic soft decoding. Moreover, the performance advantage of elliptic codes over the Reed-Solomon (RS) codes is demonstrated. © 1972-2012 IEEE.

关键词： Interpolation

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Stock Prediction Using Optimized LightGBM Based on Cost Awareness 5

Stock Prediction Using Optimized LightGBM Based on Cost Awar...

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5th IEEE International Conference on Cybernetics, CYBCONF 2021

作者： Zhao, Xiaosong Zhao, Qiangfu University of Aizu System Intelligence Laboratory Aizu Fukushima965-8580 Japan University of Aizu Graduate School of Computer Science and Engineering Aizu Fukushima965-8580 Japan

ISBN: (纸本)9781665403207

The application of machine learning in stock market prediction has generated widespread attention. This paper proposes a new method for short-term stock investment using optimized LightGBM based on cost awareness. By cost awareness here we define that the decision process is more aware of the false-positive errors, or is more aware of 'fake chances', and the cost for investment can be reduced. The main contribution of the research is to design an investment method for stock price prediction. Based on the principles of short-term investment and current quantitative investment research, we select a series of technical indicators that meet the requirements of this research to improve the reliability of the prediction results. We propose the concept of cost awareness to improve the accuracy of prediction. By comparing with the previous results, we demonstrated that the performance of the optimized model based on cost awareness could be improved significantly. we eventually examined and compared results with XGBoost and Random Forest, and found that LightGBM provides better performances in prediction accuracy, profitability, and risk control ability. © 2021 IEEE.

关键词： Investments

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Synergistic Optimization of Power & Capacity Configuration Considering Control Strategy for Flywheel Energy Storage Participating in Primary Frequency Regulation

SSRN

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SSRN 2024年

作者： Liang, Lu Hong, Feng Ji, Weiming Jiang, Hui Hao, Junhong Fang, Fang Liu, Jizhen The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education School of Energy Power and Mechanical Engineering North China Electric Power University Beijing102206 China

The intermittent and stochastic nature of renewable energy sources poses stern challenges to the frequency stability of new power system. The flywheel energy storage system (FESS), with its rapid response and high power density, plays a pivotal role in mitigating frequency fluctuations. To improve the performance of the FESS assisting thermal power unit (TPU), a collaborative optimization methodology of the capacity configuration and control strategy for FESS is presented in this paper. Aiming to enhance the primary frequency control (PFC) performance and reduce the life-cycle cost of the FESS, this paper established a capacity configuration model based on a dynamic adaptive control strategy, in which FESS compensates for the lack of TPU frequency regulation capability and recovery SOC actively. A case study demonstrated that the optimization model proposed is suitable for different frequency scenarios. It has been verified that the control strategy which is based on the SOC of FESS and frequency regulation capability of the TPU is economically more attractive compared to traditional strategies. In addition, this study revealed that a FESS with a charge/discharge rate of 7.5 C and rated power of 7.6 MW was appropriate for assisting a 600MW TPU in PFC by the optimal capacity configuration and control strategy. © 2024, The Authors. All rights reserved.

关键词： Wheels

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Validating SMT Solvers via Skeleton Enumeration Empowered by Historical Bug-Triggering Inputs

Validating SMT Solvers via Skeleton Enumeration Empowered by...

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International Conference on Software engineering (ICSE)

作者： Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Huazhong University of Science and Technology China

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Robin: A Novel Method to Produce Robust Interpreters for Dee...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zhen Li Ruqian Zhang Deqing Zou Ning Wang Yating Li Shouhuai Xu Chen Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security Department of Computer Science University of Colorado Colorado Springs USA Center for Research in Computer Vision University of Central Florida USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.

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A 7-Channel Bio-Signal Analog Front End Employing Single-End Chopping Amplifier Achieving 1.48 NEF

A 7-Channel Bio-Signal Analog Front End Employing Single-End...

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European Conference on Solid-State Circuits (ESSCIRC)

作者： Renze Gan Liangjian Lyu C.-J. Richard Shi State Key Laboratory of ASIC and System Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China Department of Electrical and Computer Engineering University of Washington Seattle WA USA

This paper presents a high noise efficiency readout circuit for multi-channel bio-signal acquisition. The prototype is implemented in a 65 nm CMOS LP process, which achieves a power consumption of 2.06 $\mu$W/channel, a noise efficiency factor (NEF) of1.48, a common-mode rejection ratio (CMRR) of 105 dB at 50 Hz, and a crosstalk of -65dB by employing these new techniques, including 1) the use of single-ended amplifiers as the unit of low noise amplifier (LNA) for lower NEF, 2) single-end chopping techniques for single-ended amplifiers for suppressing low-frequency noise and offset, 3) digital-assisted DC servo loop (DDSL) for fast settling.

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Scalable Distributed Organizational Structure and Self-Organization Mechanisms for Unmanned system Cluster 9

Scalable Distributed Organizational Structure and Self-Organ...

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9th IEEE International Conference on computer Science and Network Technology, ICCSNT 2021

作者： Liu, Haibo Lin, Yang Shen, Jing Wang, Xing Shi, Changting Zhang, Jiayu College of Computer Science And Technology Harbin Engineering University Harbin150001 China Science And Technology On Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781665443647

Reasonable control organizational structure can help an unmanned system cluster cooperate more effectively to complete tasks. Previous research of existing organizational structures has problems implementing the task in a natural environment, such as the cluster's lack of scalability. This paper proposes a three-layer scalable distributed organizational structure and dynamic self-organization mechanisms, which overcomes the lack of scalability and ensures that the cluster can still perform stably after the cluster size changes. The simulation results show that the organizational structure realizes information sharing and cross-layer control, which enhance the flexibility and stability of unmanned system clusters. Comparison results show that our achievements can make a good performance, contributing to this area of future research. © 2021 IEEE.

关键词： Scalability

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